In this study, we compared our proposed OOD detection method with a common strategy based on prediction uncertainty [1]}. Specifically, we trained our models using dropout (with a rate of 10%) after all convolutional layers. At test time, we drew \(N=10\)  random dropout masks and computed the average of these segmentation probability maps. We used the entropy of this mean probability map, \(H(\bar{p})= - \bar{p} \log (\bar{p})\)  as an estimated voxel-wise map of prediction uncertainty. To estimate an image-wise uncertainty, as suggested in [2]}, we used the average of the voxel-wise uncertainty on the predicted foreground. Similar to our approach with \(\text{OODM}\)  explained above, we computed a threshold similar to Eq. (REF ) on the training set. This threshold was used to determine if a test image was OOD.
